期刊
SOFT COMPUTING
卷 24, 期 6, 页码 4441-4453出版社
SPRINGER
DOI: 10.1007/s00500-019-04206-w
关键词
Reinforcement learning; Sequential Ordering Problem; Factorial design; Scott-Knott method; Tuning parameters
In this paper, we present a technique to tune the reinforcement learning (RL) parameters applied to the sequential ordering problem (SOP) using the Scott-Knott method. The RL has been widely recognized as a powerful tool for combinatorial optimization problems, such as travelling salesman and multidimensional knapsack problems. It seems, however, that less attention has been paid to solve the SOP. Here, we have developed a RL structure to solve the SOP that can partially fill that gap. Two traditional RL algorithms, Q-learning and SARSA, have been employed. Three learning specifications have been adopted to analyze the performance of the RL: algorithm type, reinforcement learning function, and epsilon parameter. A complete factorial experiment and the Scott-Knott method are used to find the best combination of factor levels, when the source of variation is statistically different in analysis of variance. The performance of the proposed RL has been tested using benchmarks from the TSPLIB library. In general, the selected parameters indicate that SARSA overwhelms the performance of Q-learning.
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